{"id":"W2986912224","doi":"10.1109/poweri.2014.7117664","title":"Multi step ahead forecasting of wind power by genetic algorithm based neural networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Mean absolute percentage error; Time series; Wind power; Computer science; Mean squared error; Genetic algorithm; Feedforward neural network; Algorithm; Metric (unit); Series (stratigraphy); Wind speed; Performance metric; Artificial intelligence; Machine learning; Statistics; Engineering; Mathematics; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001364902,0.0002057578,0.0002253594,0.00005941402,0.00004795142,0.0000235739,0.0001491519,0.0001090263,0.0001332335],"category_scores_gemma":[0.00002470498,0.0001929904,0.00008074412,0.0001530164,0.00003210145,0.00007041724,0.00002611054,0.0001670159,0.000003729406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001617312,"about_ca_system_score_gemma":0.000004666265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005312263,"about_ca_topic_score_gemma":0.00001746569,"domain_scores_codex":[0.9989414,0.00003034104,0.0003247054,0.0001797247,0.0001361381,0.0003877483],"domain_scores_gemma":[0.999459,0.0001384759,0.00004893209,0.0002086448,0.00003686528,0.0001080967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002232048,0.00001596717,0.000897763,0.00002242576,0.00001692373,0.000002045507,0.00003464876,0.8745574,0.0007362156,0.000007084012,0.0008864701,0.1228208],"study_design_scores_gemma":[0.0004833495,0.00006932895,0.0005175195,0.0000371149,0.00001087246,0.000005655906,0.00001367224,0.994775,0.001634416,0.000002029381,0.002231667,0.0002193869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1294846,0.0003449232,0.8641521,0.000009213517,0.0005751622,0.0000830627,0.000008624941,0.0002259683,0.005116292],"genre_scores_gemma":[0.9043636,0.000002553116,0.09520971,0.00007584616,0.0001091264,0.000003024859,0.00001684749,0.00005389555,0.0001653474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.774879,"threshold_uncertainty_score":0.786992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092932762841075,"score_gpt":0.1941752539720267,"score_spread":0.1832459263436159,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}